A Pragmatic Approach Of Heart AndLiver Disease Prediction UsingMachine Learning Classifiers
Abstract
Heart and liver diseases are major contributors to global mortality, presenting significant challenges for healthcare systems. Cardiovascular conditions disrupt blood flow and can lead to critical issues such as heart attacks and strokes. Likewise, liver diseases affect millions worldwide, often progressing to severe complications like cirrhosis and liver failure, accounting for nearly two million deaths annually. Timely diagnosis and treatment are essential in lowering fatality rates and improving patient health outcomes. With the evolution of Artificial Intelligence (AI) and Machine Learning (ML), predictive models have become instrumental in improving disease detection and management. This research investigates various machine learning classifiers for predicting heart and liver diseases, leveraging diverse datasets for in-depth analysis. By assessing multiple algorithms, this study aims to determine the most effective predictive model, enhancing early detection, enabling prompt medical intervention, and ultimately reducing the global impact of these diseases.
Keywords
Heart Conditions, Liver Disorders, Cardiovascular Diseases, Machine Learning Techniques, Artificial Intelligence Applications, Predictive Analytics, Early Disease Detection, Healthcare Data Analysis, Disease Forecasting, Medical Data Classification, AI in Medicine, DataDriven Diagnostics.